Generating preference indices for image content

ABSTRACT

Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.

BACKGROUND

1. Field

The present disclosure relates generally to generating and/or providinga preference index with respect to image content, such as at least aportion of an image.

2. Information

At times, such as in advertising, market research, and so forth, it maybe useful to have an ability determine in advance how marketingmaterial, for example, may be perceived, such as by a target audience.In an attempt to determine favorability of images and/or otheradvertising materials, an advertiser, for example, may engage in acontrol-group study. However, in some instances, efforts such as thesemay consume significant periods of time and/or financial resources.

BRIEF DESCRIPTION OF DRAWINGS

Claimed subject matter is particularly pointed and/or distinctly claimedin the concluding portion of the specification. However, both as toorganization and/or method of operation, together with objects, claimedfeatures, and/or advantages thereof, claimed subject matter may beunderstood by reference to the following detailed description if readwith the accompanying drawings in which:

FIGS. 1A and 1B are diagrams showing operations which may be executed bya processor operating a neural network according to an embodiment;

FIG. 2 is a schematic diagram of a neural network, which may operateaccording to an embodiment; and

FIG. 3 is a schematic diagram of a computing platform according to anembodiment.

Reference is made in the following detailed description of theaccompanying drawings, which form a part hereof, wherein like numeralsmay designate like parts throughout to indicate corresponding and/oranalogous components. It will be appreciated that components illustratedin the figures have not necessarily been drawn to scale, such as forsimplicity and/or clarity of illustration. For example, dimensions ofsome components may be exaggerated relative to other components.Further, it is to be understood that other embodiments may be utilized.Furthermore, structural and/or other changes may be made withoutdeparting from claimed subject matter. It should also be noted thatdirections and/or references, for example, up, down, top, bottom, and soon, may be used to facilitate discussion of drawings and/or are notintended to restrict application of claimed subject matter. Therefore,the following detailed description is not to be taken to limit claimedsubject matter and/or equivalents.

DETAILED DESCRIPTION

References throughout this specification to one implementation, animplementation, one embodiment, an embodiment and/or the like means thata particular feature, structure, and/or characteristic described inconnection with a particular implementation and/or embodiment isincluded in at least one implementation and/or embodiment of claimedsubject matter. Thus, appearances of such phrases, for example, invarious places throughout this specification are not necessarilyintended to refer to the same implementation or to any one particularimplementation described. Furthermore, it is to be understood thatparticular features, structures, and/or characteristics described arecapable of being combined in various ways in one or more implementationsand, therefore, are within intended claim scope, for example. Ingeneral, of course, these and other issues vary with context. Therefore,particular context of description and/or usage provides helpful guidanceregarding inferences to be drawn.

With advances in technology, it has become more typical to employdistributed computing approaches in which portions of a problem, such assignal processing of signal samples or processing portions of a neuralnetwork, for example, may be allocated among computing devices,including one or more clients and/or one or more servers, via acomputing and/or communications network, for example. A network maycomprise two or more network devices and/or may couple network devicesso that signal communications, such as in the form of signal packetsand/or frames (e.g., comprising one or more signal samples), forexample, may be exchanged, such as between a server and a client deviceand/or other types of devices, including between wireless devicescoupled via a wireless network, for example.

An example of a distributed computing system is the Hadoop distributedcomputing system, which employs a map-reduce type of architecture. Inthis context, the terms map-reduce architecture and/or similar terms areintended to refer a distributed computing system implementation forprocessing and/or for generating large sets of signal samples employinga parallel, distributed process performed over a network of individualcomputing devices. A map operation and/or similar terms refer toprocessing of signals to generate one or more key-value pairs and todistribute the one or more pairs to the computing devices of thenetwork. A reduce operation and/or similar terms refer to processing ofsignals via a summary operation (e.g., such as counting the number ofstudents in a queue, yielding name frequencies). A system may employsuch an architecture for processing by marshalling distributed servers,running various tasks in parallel, and managing communications andsignal transfers between various parts of a neural network, in anembodiment. (See, for example Jeffrey Dean et al. “Large ScaleDistributed Deep Networks,” Advances in Neural Information ProcessingSystems 25, 2012, pp. 1232-1240.) As mentioned, one non-limiting, butwell-known example, is the Hadoop distributed computing system, whichrefers to an open source implementation of a map-reduce typearchitecture, but may include other aspects, such as the Hadoopdistributed file system (HDFS). In general, therefore, Hadoop and/orsimilar terms refer to an implementation scheduler for executing largeprocessing jobs using a map-reduce architecture.

It should be understood that for ease of description, a network device(also referred to as a networking device) may be embodied and/ordescribed in terms of a computing device. However, it should further beunderstood that this description should in no way be construed to implyor suggest that claimed subject matter is limited to one embodiment,such as a computing device and/or a network device, and, instead, may beembodied as a variety of devices or combinations thereof, including, forexample, one or more illustrative examples.

Likewise, in this context, the terms “coupled”, “connected,” and/orsimilar terms are used generically. It should be understood that theseterms are not intended as synonyms. Rather, “connected” is usedgenerically to indicate that two or more components, for example, are indirect physical, including electrical, contact; while, “coupled” is usedgenerically to mean that two or more components are potentially indirect physical, including electrical, contact; however, “coupled” isalso used generically to also mean that two or more components are notnecessarily in direct contact, but nonetheless are able to co-operateand/or interact. The term “coupled” is also understood generically tomean indirectly connected, for example, in an appropriate context.

The terms, “and”, “or”, “and/or” and/or similar terms, as used herein,include a variety of meanings that also are expected to depend at leastin part upon the particular context in which such terms are used.Typically, “or” if used to associate a list, such as A, B, or C, isintended to mean A, B, and C, here used in the inclusive sense, as wellas A, B, or C, here used in the exclusive sense. In addition, the term“one or more” and/or similar terms is used to describe any feature,structure, and/or characteristic in the singular and/or is also used todescribe a plurality and/or some other combination of features,structures and/or characteristics. Likewise, the term “based on” and/orsimilar terms are understood as not necessarily intending to convey anexclusive set of factors, but to allow for existence of additionalfactors not necessarily expressly described. Of course, for all of theforegoing, particular context of description and/or usage provideshelpful guidance regarding inferences to be drawn. It should be notedthat the following description merely provides one or more illustrativeexamples and claimed subject matter is not limited to these one or moreillustrative examples; however, again, particular context of descriptionand/or usage provides helpful guidance regarding inferences to be drawn.

A network may also include now known, and/or to be later developedarrangements, derivatives, and/or improvements, including, for example,past, present and/or future mass storage, such as network attachedstorage (NAS), a storage area network (SAN), and/or other forms ofcomputing and/or device readable media, for example. A network mayinclude a portion of the Internet, one or more local area networks(LANs), one or more wide area networks (WANs), wire-line typeconnections, wireless type connections, other connections, or anycombination thereof. Thus, a network may be worldwide in scope and/orextent. Likewise, sub-networks, such as may employ differingarchitectures and/or may be compliant and/or compatible with differingprotocols, such as computing and/or communication protocols (e.g.,network protocols), may interoperate within a larger network. In thiscontext, the term “sub-network” and/or similar terms, if used, forexample, with respect to a network, refers to the network and/or a partthereof. Sub-networks may also comprise links, such as physical links,connecting and/or coupling nodes so as to be capable to transmit signalpackets and/or frames between devices of particular nodes includingwired links, wireless links, or combinations thereof. Various types ofdevices, such as network devices and/or computing devices, may be madeavailable so that device interoperability is enabled and/or, in at leastsome instances, may be transparent to the devices. In this context, theterm “transparent” refers to devices, such as network devices and/orcomputing devices, communicating via a network in which the devices areable to communicate via intermediate devices of a node, but without thecommunicating devices necessarily specifying one or more intermediatedevices of one or more nodes and/or may include communicating as ifintermediate devices of intermediate nodes are not necessarily involvedin communication transmissions. For example, a router may provide a linkand/or connection between otherwise separate and/or independent LANs. Inthis context, a private network refers to a particular, limited set ofnetwork devices able to communicate with other network devices in theparticular, limited set, such as via signal packet and/or frametransmissions, for example, without a need for re-routing and/orredirecting transmissions. A private network may comprise a stand-alonenetwork; however, a private network may also comprise a subset of alarger network, such as, for example, without limitation, all or aportion of the Internet. Thus, for example, a private network “in thecloud” may refer to a private network that comprises a subset of theInternet, for example. Although signal packet and/or frame transmissionsmay employ intermediate devices of intermediate nodes to exchange signalpacket and/or frame transmissions, those intermediate devices may notnecessarily be included in the private network by not being a source ordestination for one or more signal packet and/or frame transmissions,for example. It is understood in this context that a private network mayprovide outgoing network communications to devices not in the privatenetwork, but such devices outside the private network may notnecessarily be able to direct inbound network communications to devicesincluded in the private network.

The Internet refers to a decentralized global network of interoperablenetworks that comply with the Internet Protocol (IP). It is noted thatthere are several versions of the Internet Protocol. Here, the term“Internet Protocol,” “IP,” and/or similar terms, is intended to refer toany version, now known and/or later developed of the Internet Protocol.The Internet includes local area networks (LANs), wide area networks(WANs), wireless networks, and/or long haul public networks that, forexample, may allow signal packets and/or frames to be communicatedbetween LANs. The term “World Wide Web” (“WWW” or “Web”) and/or similarterms may also be used, although it refers to a part of the Internetthat complies with the Hypertext Transfer Protocol (HTTP). For example,network devices may engage in an HTTP session through an exchange ofappropriately compatible and/or compliant signal packets and/or frames.It is noted that there are several versions of the Hypertext TransferProtocol. Here, the term “Hypertext Transfer Protocol,” “HTTP,” and/orsimilar terms is intended to refer to any version, now known and/orlater developed. It is likewise noted that in various places in thisdocument substitution of the term “Internet” with the term “World WideWeb” (Web′) may be made without a significant departure in meaning andmay, therefore, not be inappropriate in that the statement would remaincorrect with such a substitution.

Although claimed subject matter is not in particular limited in scope tothe Internet and/or to the Web; nonetheless, the Internet and/or the Webmay without limitation provide a useful example of an embodiment atleast for purposes of illustration. As indicated, the Internet and/orthe Web may comprise a worldwide system of interoperable networks,including interoperable devices within those networks. The Internetand/or Web has evolved to a public, self-sustaining facility that may beaccessible to tens of millions of people or more worldwide. Also, in anembodiment, and as mentioned above, the terms “WWW” and/or “Web” referto a part of the Internet that complies with the Hypertext TransferProtocol. The Internet and/or the Web, therefore, in this context, maycomprise an service that organizes stored content, such as, for example,text, images, video, etc., through the use of hypermedia, for example. AHyperText Markup Language (“HTML”), for example, may be utilized tospecify content and/or to specify a format for hypermedia type content,such as in the form of a file and/or an “electronic document,” such as aWeb page, digital image, a contiguous portion of the digital image, justto name a few examples. An Extensible Markup Language (“XML”) may alsobe utilized to specify content and/or format of hypermedia type content,such as in the form of a file or an “electronic document,” such as a Webpage, in an embodiment. Of course, HTML and/or XML are merely examplelanguages provided as illustrations. Furthermore, HTML and/or XML(and/or similar terms) is intended to refer to any version, now knownand/or later developed of these languages. Likewise, claimed subjectmatter is not intended to be limited to examples provided asillustrations, of course.

As used herein, the term “Web site” and/or similar terms refer to acollection of related Web pages. Also as used herein, “Web page” and/orsimilar terms, refer to any electronic file and/or electronic document,such as an electronic digital image, and/or a contiguous portion of anelectronic digital image, such as may be accessible via a network,including by specifying a URL for accessibility via the Web, forexample. As alluded to above, in one or more embodiments, a Web page maycomprise content coded using one or more languages, such as, forexample, markup languages, including HTML and/or XML, although claimedsubject matter is not limited in scope in this respect. Also, in one ormore embodiments, application developers may write code in the form ofJavaScript, for example, to provide content to populate one or moretemplates, such as for an application. The term ‘JavaScript’ and/orsimilar terms are intended to refer to any now known and/or laterdeveloped version of this programming language. However, JavaScript ismerely an example programming language. As was mentioned, claimedsubject matter is not intended to be limited to examples and/orillustrations.

As used herein, the terms “index”, “preference index”, “sentimentindex,” “document”, “electronic document”, “content”, “digital image”,and/or similar terms are meant to refer to signals and/or states in aphysical format, such as a digital signal and/or digital state format,e.g., that may be perceived by a user if displayed, played and/orotherwise executed by a device, such as a digital device, including, forexample, a computing device, but otherwise might not necessarily beperceivable by humans (e.g., in a digital format). Likewise, in thiscontext, content (e.g., digital content) provided to a user in a form sothat the user is able to perceive the underlying content itself (e.g.,hear audio or see images, as examples) is referred to, with respect tothe user, as ‘consuming’ content, ‘consumption’ of content, ‘consumable’content and/or similar terms. For one or more embodiments, an electronicdocument may comprise a Web page coded in a markup language, such as,for example, HTML (hypertext markup language).

In another embodiment, an electronic document may comprise a portion ora region of a Web page, a portion, such as a contiguous portion, of adigital image captured using a digital camera. However, claimed subjectmatter is not intended to be limited in these respects. Also, for one ormore embodiments, an electronic document and/or digital image maycomprise a number of components. Components in one or more embodimentsmay comprise text, for example, in the form of physical signals and/orphysical states (e.g., capable of being physically displayed). Also, forone or more embodiments, components may comprise a graphical object,such as, for example, an image, such as a digital image, and/or acontiguous portion of digital image, which, again, comprise physicalsignals and/or physical states (e.g., capable of being physicallydisplayed). In an embodiment, content may comprise, for example, text,images, audio, video, and/or other types of electronic documents and/orportions thereof, for example.

Also as used herein, one or more parameters may be descriptive of acollection of signal samples, and exist in the form of physical signalsand/or physical states, such as memory states. For example, one or moreparameters, such as parameters of a neural network, may comprise signalsample values utilized in a convolutional filters, signal sample valuesutilized in one or more kernel masks, response normalization of outputsignal samples of one or more neurons of a neural network, neuronweighting value levels, size of filters, number of filters, and soforth. Claimed subject matter is intended to embrace meaningful,descriptive parameters in any format, so long as the one or moreparameters comprise physical signals.

Signal packets and/or frames, also referred to as signal packettransmissions and/or signal frame transmissions, may be communicatedbetween nodes of a network, and/or among neurons of a neural network,where a node may comprise one or more network devices and/or one or morecomputing devices, for example. As an illustrative example, but withoutlimitation, a node may comprise one or more sites employing a localnetwork address. Likewise, a device, such as a network device and/or acomputing device, may be associated with a computing node. A signalpacket and/or frame may, for example, be communicated via acommunication channel and/or a communication path, such as comprising aportion of the Internet and/or the Web, from a site via an access nodecoupled to the Internet. Likewise, a signal packet and/or frame may beforwarded via network nodes to a target site coupled to a local network,for example. A signal packet and/or frame communicated via the Internetand/or the Web, for example, may be routed via a path comprising one ormore gateways, servers, etc. that may, for example, route a signalpacket and/or frame in accordance with a target and/or destinationaddress and availability of a network path of network nodes to thetarget and/or destination address. Although the Internet and/or the Webcomprises a network of interoperable networks, not all of thoseinteroperable networks are necessarily available and/or accessible tothe public. A computing network may be very large, such as comprisingthousands of nodes, millions of nodes, billions of nodes, or more, asexamples.

Media networks, such as the Yahoo!™ network, for example, may beincreasingly seeking ways to attract users to their networks and/or toretain users within their networks for extended periods of time. A medianetwork may, for example, comprise an Internet Web site and/or group ofWeb sites having one or more sections. For instance, the Yahoo!™ networkincludes Web sites located within different categorized sections, suchas sports, finance, current events, and games, to name just a fewnon-limiting examples among a variety of possible examples. To attractand/or retain users within its network, Yahoo!™ and/or other medianetworks may continually strive to provide content relating tocategorized sections that may be interesting and/or of use to users.

As more users remain within a media network for extended periods oftime, a media network may become more valuable to potential advertisers.Thus, typically, advertisers may be inclined to pay more money and/orprovide other considerations to a media network in return foradvertising to users, for example, via that media network, its partners,and/or subsidiaries. In an implementation, a user may beneficiallyinteract with a media network to determine a “preference index” withrespect to digital content, such as a contiguous portion of a capturedimage, for example, which may influence whether a user “posts,” forexample, an image, or portion thereof, on a user's blog, social networkpage, etc. (e.g., Tumblr, Flickr, and so forth). In otherimplementations, a social network user may wish to find images that maybe likely to bring about an above-average preference index to assist inviral marketing, for example. In these implementations, and others,assessing whether a user's impression of content, such as a capturedimage or a contiguous portion thereof, for example, suggests a positiveor a negative “preference,” again, for example, may be helpful, such asfor content to be displayed to an audience. In this context, a“preference index” and/or similar terms refer, at least in part, to ameasurement of a feeling and/or an emotion towards displayable contentor displayed content, such as a contiguous portion of a digital image.It is further noted, in this context, that a measurement is meant tocomprise a measurement with respect to the displayed or displayablecontent as a whole. Thus, for an entire image, for example, ameasurement refers to a measurement as to the entire image as thedisplayable content is meant to be displayed; likewise, for a contiguousportion (e.g., sub-portion) of an image, a measurement is as to thecontiguous portion as a whole, again, as the displayable content ismeant to be displayed. Likewise, in this context, displayable ordisplayed content is understood to imply content capable of beingperceived visually; however, a device, for example, may be employed sothat content, such as stored content, is rendered in a manner to bevisually perceived, whereas content as stored may not necessarily bevisually perceivable. Ascertaining a preference index, may comprise,among other things, one or more signal samples measurements of a user'ssentiment, which may be helpful in assessing, in advance, an audience'sresponse, for example.

In one or more embodiments, a “preference index” may be expressed usinga numerical scale, such as a scale allowing discrete, integer-valuedincremental levels, from, for example, −2.0 to +2.0. In an embodiment, apreference index comprising a signal sample value level of −2.0 may atleast approximately indicate that an image, or contiguous portionthereof, for example, may be more likely to evoke a substantiallynegative preference for an individual viewing it. In an embodiment, apreference index comprising a signal sample value level of +2.0 mayindicate an image, or contiguous portion thereof, for example, may atleast approximately be more likely to evoke a substantially positivepreference for an individual viewing it. Preference indices comprisingsignal sample value levels of −1.0, 0.0, and +1.0 respectively mayindicate an image that may at least approximately be more likely toevoke a relatively mild negative preference, a relatively neutralpreference, and a relatively mild positive preference, for example.Thus, as implied, a preference index may include the opposite of beingpreferred, for example, such as a negative preference. It is noted that,as implied from the foregoing, a scale, such as the foregoingillustrative example, is understood to be approximate and to be ordinal.

In certain embodiments, a preference index with respect to displayabledigital content, such as a contiguous portion of captured image, forexample, may include, at least in part, a relative likelihood of evokingpositive sentiments, negative sentiments, or neutral sentiments, such asresponsive to a user viewing the particular content. However, in certainembodiments, in addition, a preference index may likewise furthercomprise a host of possible additional perceived qualities and/orattributes with respect to displayable content in addition to (e.g., inconjunction with) “sentiment” per se. Accordingly, a preference indexmay comprise a signal sample measurement corresponding to sentimentalong with, just to name a few possible examples, whether particularcontent may be perceived, relatively speaking, as “expensive” or“inexpensive”, as “healthy” or “unhealthy,” “old” or “new,” and soforth. Accordingly, although likelihood of evoking a particularsentiment and/or range of sentiments may be utilized to generate asignal sample measurement for a preference index, claimed subject matteris intended to embrace a wide range of additional contributors alongwith sentiment.

In certain embodiments, a preference index may furthermore be expressedutilizing scales other than integer-valued incremental levels, such asincluding levels other than signal sample integer-valued levels. Forexample, particular embodiments may involve generation of signal samplevalue levels for a preference index approximately in a range ofapproximately −1.0 to approximately +1.0 (e.g., a signal sample valuelevel of approximately −1.0 indicates a negative preference and a signalsample value level of approximately +1.0 indicates a positivepreference). Additionally, signal sample value levels of a preferenceindex may be expressed utilizing decimal-valued levels, such as −1.1,−0.9, and so forth, in which, for example, larger negativedecimal-valued levels may indicate a stronger negative preference, andin which, for example, larger positive decimal value levels may indicatea stronger positive preference. Further, signal sample value levels of apreference index may be expressed utilizing a group of characters, suchas, for example, alphabetical characters, e.g., “A,” “B,” “C,” “D,” “E,”in which “A,” may indicate a relatively strong positive preference, and“E,” may indicate a relatively strong preference, for example, or viceversa. Accordingly, it should be noted that any number of indicatortypes may be used to express signal sample value levels of a preferenceindex, such as whole-numbered value levels, integer-valued levels,decimal-valued levels, rational-numbered value levels,alphabetically-valued levels, or other schemes for assigning valuelevels to preference indices, or combinations thereof. Thus, claimedsubject matter is not intended to be limited in this respect. As above,it is noted here that a scale for an index, such these illustrativeexamples, are understood to be approximate and to be ordinal.

Typically, training via a classifier, such as a machine learningclassifier (which may comprise, for example, a support vector machine(SVM)), for example, to generate a preference index, for example, ingeneral, may be complex and/or time intensive. For example, measurementsfor a large sample of digital content would typically be employed. Thus,measurements would be gathered and stored. Ground truth would alsotypically be employed, meaning verification using a sample set where itis believed that reasonably correct measurements exist. Thus, inaddition to time and cost, computational and/or memory resources may beconsumed in connection with implementation and validation. An approachto generate similar results with less effort and/or complexity mayemploy a neural network, however, may include, for example a neural suchas that described by Krizhevsky et al, “ImageNet Classification withDeep Convolutional Neural Networks,” Advances in Neural InformationProcessing Systems 25 (NIPS 2012).

In certain embodiments, a neural network, which may comprise, forexample, a network of “neurons,” may be employed to generate signalsample values for preference indices of contiguous portions of digitalimages. A neural network may comprise, for example, dozens, hundreds,thousands, or a greater number of neurons, which may produce and/orgenerate one or more output signal samples as a function of one or moreinput signal samples, for example. Thus, a neuron of a neural network,in an embodiment, may generate an output signal sample, such as f(x),responsive to one or more input signal samples, such as f(z₁), f(z₂),f(z₃), and so forth. In particular embodiments, neurons of a neuralnetwork may generate, for example, an output signal sample responsive toexecuting a weighted superposition (e.g., summing) operation utilizinginput signal samples f(z₁), f(z₂), and f(z₃), such as shown inexpression 1 below:f(x)=w ₁ f(z ₁)+w ₂ f(z ₂)+w ₃ f(z ₃)  (1)In expression 1, weights w₁, w₂, and w₃, may comprise, for example, avalue level approximately in the range of approximately 0.0 toapproximately 1.0.

Thus, in an embodiment, rather than training a large sample set ofdigital content, in effect, instead, a sample set employed to generate aneural network, here and typically, a large sample, may be leveraged,such as to generate a preference index, for example. In an embodiment asan illustration, at a high level, a neural network is developed fordigital content using a large number of images and/or portions thereof,for example, to perform image classification. Likewise, a relativelysmall sample of digital images may be employed to generate preferenceindices. In this example, using the relatively small sample of digitalimages (at least in comparison with the sample used to generate theneural network), a neural network may be employed to generate neuralnetwork signal samples. It is noted that modifications may be made toprocessing of the neural network to generate signal samples for use withpreference indexing. However, these signal samples and preferenceindices measurements for the relatively small sample of digital imagesmay be employed to train one or more classifiers, such as, for example,a machine learning classifier in an embodiment; however, likewise, othertypes of processing may also be employed for the neural networkgenerated signal samples and the reference indices measurements, asdescribed later. Leveraging a neural network in this manner in thiscontext is referred to as transfer learning.

A neural network may, in an embodiment, be implemented using one or moresets of executable instructions capable of being executed by one or moreaccessible computing devices, for example, which may operate usingphysical signals and/or physical states, such as physical statescorresponding to non-transitory physical memory states, for example. Itis noted that the term signal samples and/or similar terms throughoutthis patent application are understood to refer to physical signalsand/or physical states, such as physical states corresponding tonon-transitory physical memory states. A neural network may comprise,for example, one or more input signal layers, which may refer to one ormore network layers to receive and/or access input signal samples. Aneural network may additionally comprise one or more output signallayers, which may refer to a network layer that generates observableoutput signal samples, such as in the form of an output parameter file,which may be accessible by one or more sets of computer-executableinstructions, such as operating external to the neural network, forexample. A neural network may further comprise one or more “hidden”layers, which may refer to one or more layers of a neural network toperform signal processing. It is noted that a distributed computingsystem may, in some cases, may be employed, but is generally notrequired.

In certain embodiments, a neural network may, for example, involve useof one or more convolutional filtering layers, in which input signalsamples, which may comprise a digital image or a contiguous portion of adigital image, may be convolved with one or more kernel operations inwhich the one or more kernel operations specify, in effect, digitalsignal processing of the content being processed, in this example. Inparticular embodiments, convolutional filtering may be executed usingsub-regions of a contiguous portion of a captured digital image, forexample. In particular embodiments, a neural network may comprise, forexample, five successive convolutional filtering layers, in which, asdescribed in greater detail below, may operate utilizing signal samplesfrom previous convolutional filtering layers. Of course, it isunderstood that any neural network may be employed and claimed subjectmatter is not limited in scope to the particulars of the illustrativeexample provided.

In certain embodiments, signal samples may undergo various stages ofsignal processing, such as in connection with processing by a neuralnetwork, including, for example, nonlinear filtering and/or max-pooling,which may, in an embodiment, take place prior to signal samples beingprocessed by a convolutional filtering layer, after signal samples aregenerated by a convolutional layer, and/or during signal processing by aconvolutional layer, for example. As shown in FIG. 1A, in an embodiment100, nonlinear filtering of signal samples from a convolutionalfiltering layer may involve use of rectification. In general,rectification and/or similar terms, in this context, are understood torefer to permitting a signal in one direction but not an opposingdirection. In this context, therefore, negative signals are cut off ortruncated while positive signals are permitted or passed through. For asignal sample x, for example, rectification may be described as beingsubstantially in accordance with max(0,x). However, in otherembodiments, filtering of signal samples may involve filtering usingother linear and/or nonlinear approaches, which may operate in place ofor in addition to rectification. Thus, claimed subject matter is notlimited in scope in this respect.

Likewise, as illustrated in FIG. 1B, in an embodiment 150, signalsamples, such as, for example, from convolutional filtering layer 160,as an illustrative example, may undergo max-pooling in convolutionalfiltering layer 170. In this context, max-pooling and/or similar termsrefer to a type of nonlinear down-sampling in which signal samplescorresponding to a portion of an image may be partitioned intosub-regions comprising one or more non-overlapping rectangles, forexample. In one non-limiting example, such as FIG. 1B, signal samplevalues of a sub-region from a convolutional filtering layer, such assignal sample values 162 and 164 of layer 160, may be “pooled” into, forexample, a signal sample value, such as signal sample value 172 of layer170. Likewise, signal sample value 166 of convolutional filtering layer160 may be pooled to form signal sample value 176 of convolutionalfiltering layer 170. Thus, in this illustrative example, in layer 170, asub-region takes as its value a maximum value of the correspondingsub-region of layer 160.

In some embodiments, successive convolutional filtering layers mayexecute image processing of signal sample values which may, for example,correspond to a contiguous portion of a captured image. Signal samplesgenerated at one convolutional filtering layer may comprise signalsamples for use by another filtering layer, such as of a neural network.Thus, in this context, the term fully-connected filtering layers of aneural network and/or similar terms refers to substantially all neuronsof a previous layer coupling to all neurons of a subsequent layer.However, it should be noted that this is but one way of arrangingneurons, and claimed subject matter is not limited in this respect. Inparticular embodiments, such an arrangement may permit processing ofsubstantially all signal sample outputs from a previous fully-connectedlayer by one or more subsequent fully-connected layers, for example. R

As mentioned previously, in an embodiment, a neural network may bedeveloped, such as to perform image classification. In particularembodiments, thousands, hundreds of thousands, millions, or a greaternumber of samples of image content may be utilized. In one or moreembodiments, a neural network comprising five convolutional filteringlayers and three connected layers may comprise approximately 650,000neurons, which may utilize approximately 60,000,000 parameters. In onepossible example, as will be described with reference to FIG. 2, aneural network may perform image classification. Thus, input signalsamples from an image corresponding to a zebra may generate anappropriate object label. A neural network may, thus, label input signalsamples corresponding to a large variety of other types of images, andclaimed subject matter is not limited in this respect. (See, forexample, Krizhevsky et al., discussed previously, supra.)

A neural network utilized to generate signal sample values correspondingto image object labels for captured images may likewise be employed togenerate preference indices for digital content substantially inaccordance with transfer learning. Thus, for a sample of digitalcontent, preference indices may be compiled and neural network signalsamples may be generated. As mentioned previously, in an embodiment,modifications may be made to processing by a neural network to generatesignal samples for use with preference indexing. Accordingly, signalsamples generated by a neural network to correspond to the sample ofdigital content for which preference indices have been compiled maypermit generation of a preference index for digital content not includedin the sample. As previously described, such a capability may have avariety of uses, such as, for example, determining whether a user should“post,” for example, an image on a user's blog or social network page.In another example, determining a preference index for digital contentmay assist in viral marketing, or may be helpful in personalizingcontent to be displayed to an audience, just to name a few examples.

FIG. 2 is a schematic diagram of a neural network, which may operatesubstantially according to an embodiment 200. Although two distinctneural networks are identified in FIG. 2, this is for the convenience ofexplanation. In implementation, typically one neural network, perhapseven selected sub-portions thereof, may be employed. Nonetheless,continuing with this illustrative example, in at least some embodiments,a first digital image group, which may comprise digital images 205A,205B, . . . , 205N may differ from a second digital image group, whichmay comprise digital images 250A, 250B, . . . , 250N.

In the embodiment of FIG. 2, a first digital image group may be used todevelop a neural network to generate signal sample values correspondingto one or more object labels for a digital image, previously discussedas image classification. Thus, neural network parameters, such asdescribed below, may subsequently be used by a neural network togenerate signal sample values corresponding to one or more preferenceindices for a digital image, for example. As mentioned, somemodifications may be made, if appropriate, to the developed neuralnetwork processing, since, here, for example, image classification isnot the same as generating a preference index.

It should also be noted that in particular embodiments, generation ofsignal sample values corresponding to preference indices for digitalimages may be performed in a term-independent manner, which refers togenerating a preference index for digital content, such as a contiguousportion of a digital image, independent of language. For example, in oneor more embodiments, a digital image, for example, may comprise anabstract shape, which may not easily conform to a description as one ormore terms of a given language. In another example, a digital image, forexample, may comprise two or more discernible shapes present in a singleimage. As mentioned previously, generation of a preference index in sucha case would be for the image as a whole in this example, rather thanfor the independent objects. It should further be noted that employingtrained neural network parameters to accomplish transfer learning maybring about additional benefits, which may be utilized for otherpurposes, in addition to or in place of generating preference indices,and claimed subject matter is not limited in this respect.

Returning to FIG. 2, in certain embodiments, a neural network may employconvolutional filtering layers, such as convolutional filtering layers210, 212, 214, 216, and 218. A neural network may additionally employfully-connected layers, such as fully-connected layers 220, 222, and224. In particular embodiments, a convolutional neural network, such asshown in FIG. 2, may be described as a “deep” convolutional neuralnetwork. A deep convolutional neural network refers to a neural networkin which signal samples from one or more convolutional filtering layers,such as convolutional filtering layer 210, are used to provide signalsamples to one or more other convolutional filtering layers, such aslayer 212, for example, and/or one or more fully-connected layers, suchas fully-connected layer 220. In the embodiment of FIG. 2, digitalimages of a first digital image group, which may comprise digital images205A, 205B, . . . , 205N, may be divided into red-green-blue (RGB)sub-regions. In certain embodiments, substantially square sub-regions ofa digital image may be employed, which may encompass areas ofapproximately 224 pixels by approximately 224 pixels, for example,although claimed subject matter is intended to embrace sub-regions ofvirtually any number of pixels arranged in any number of shapes. Inparticular embodiments, convolutional filtering layer 210, for example,may convolve one or more sub-regions with one or more image processingkernel operations, which may permit, for example, signal processing withrespect to features of a sub-region. Convolutional filtering layers 212,214, 216, or 218, for example, may convolve signal samples of a previousconvolutional filtering layer with one or more image processing kernels.

In certain embodiments, signal samples of one or more convolutionalfiltering layers 210-218 may be filtered using rectification, an exampleof which is provided in FIG. 1. In particular embodiments, use of arectification operation may beneficially assist in training, in anembodiment. It should be noted that although use of rectification may insome situations be beneficial, other types of linear/nonlinear filteringmay be utilized, and claimed subject matter is not limited in anyparticular filtering approach. Signal samples from one or more ofconvolutional filtering layers 210, 212, 214, 216, and 218 mayadditionally be pooled using, for example, a max-pooling approach, anexample of which is provided at FIG. 1B, which, as previously noted, mayrefer to a type of nonlinear down-sampling in which signal samplescorresponding to a portion of an image may be pooled into, for example,a signal sample value level, such as a single, maximum signal samplevalue level for the image portion. In particular embodiments, filteredand max-pooled signal samples, after being generated by a layer, may beutilized by one or more additional convolutional filtering layers, suchas previously described, for example.

In FIG. 2, images 205A, . . . , 205N may comprise any number ofcontiguous images, such as an image of a zebra (205A), apple (205B), anda semi-truck (205C). In an embodiment, images 205A, . . . , 205N maycomprise a training set of more than 1,000,000 images, which may beused, for example, to develop a neural network of 60,000,000 parametersof a 650,000-neuron neural network. (See, for example, Krizhevsky etal., discussed previously, supra.) In one non-limiting embodiment, justto illustrate, convolutional filtering layers 210, 212, 214, 216, and218 may comprise 253,440 neurons, 186,624 neurons, 64,896 neurons,64,896 neurons, and 43,264 neurons, respectively, although claimedsubject matter is not limited to this illustrative example. Thus, moreor fewer neurons, for example, may be employed in a neural network. Inan embodiment, such as shown in FIG. 2, for example, subsequent toconvolving operations executed by convolutional filtering layers, signalsamples from, for example, convolutional filtering layer 218 may be usedas signal samples to be process by fully-connected layers 220, 222, and224. In one non-limiting embodiment, just to illustrate, fully-connectedlayers 220, 222, and 224 may comprise 4096 neurons, 4096 neurons, and1000 neurons, respectively, although, again, claimed subject matter isnot limited to this illustrative example. In the particular embodimentof FIG. 2, object labels may comprise, for example, object label 230,which may correspond to the zebra of digital image 205A, object label232, which may correspond to the apple of digital image 205B, and/orobject label 234, which may correspond to the semi-truck of digitalimage 205C. Thus, initially a neural network may be trained, such as viaa multinomial logistic regression objective, which may implement backpropagation by way of stochastic gradient descent, for example.

In an embodiment, however, as shown in FIG. 2, parameters of aconvolutional neural network may be transferred substantially inaccordance with transfer learning. Responsive to training of aconvolutional neural network to perform image classification for digitalimages, such as digital images 205A, 205B, and so forth, parameters maybe developed and then transferred to a neural network utilized toprovide signal sample values corresponding to preference indices fordigital images, such as digital images 250A, 250B, . . . , 250N.

In an embodiment, digital images 250A, 250B, . . . , 250N comprise a setof digital images, which may number into the thousands, millions, and soforth. However, in at least some embodiments, digital images 250A, 250B,. . . , 250N may also comprise a much smaller-sized group of digitalimages, such as less than 200, for example. In the embodiment of FIG. 2,parameters developed (e.g., via training) for use convolutionalfiltering layers 210, 212, 214, 216, and 218 to execute image objectlabeling may, at least in some embodiments, likewise be employed withconvolutional filtering layers of a neural network for use in generatingpreference indices.

In an embodiment, as mentioned, fully-connected layers 220, 222, and224, may undergo certain adjustments to be utilized in a neural networkto generate signal sample values corresponding to preference indices.For example, in a particular embodiment, fully-connected layer 222 mayprovide signal samples for use with a classifier, for example. In onepossible embodiment, fully-connected layer 222 may generate a 4096dimension signal sample vector characterization of an image. Thus, aclassifier 260 may be trained using the image set 250A, 250B, etc., inwhich the preference indices of the image set are trained with the layer222 signal sample vector characterization. Similarly, fully-connectedlayer 224 may produce a 1000 dimension signal sample vectorcharacterization. Thus, a classifier 265 may be trained using the imageset 250A, 250B, etc., in which preference indices of the image set aretrained with the layer 224 signal sample vector characterization. Itshould be noted that, in an embodiment, additional variations inarchitecture of a neural network may be implemented, and claimed subjectmatter is not limited in this respect.

In certain embodiments, as mentioned, classifiers 260 and 265, forexample, may be trained using a digital image sample set in whichpreference indices are trained using signal sample vectors generated bylayers 222 and 224, respectively. Thus, to generate a preference indexfor digital content other than the training digital images, signalsample vector characterizations of an image, or a contiguous portionthereof may be generated via a neural network and provided toclassifiers, in an embodiment.

For purposes of illustration, FIG. 3 is an illustration of an embodimentof a system 300 that may be employed in a client-server typeinteraction, such as described infra. in connection with rendering a GUIvia a device, such as a network device and/or a computing device, forexample. In FIG. 3, computing device 302 (‘first device’ in figure) mayinterface with client 304 (‘second device’ in figure), which maycomprise features of a client computing device, for example.Communications interface 330, processor (e.g., processing unit) 320, andmemory 322, which may comprise primary memory 324 and secondary memory326, may communicate by way of a communication bus, for example. In FIG.3, client computing device 302 may represent one or more sources ofanalog, uncompressed digital, lossless compressed digital, and/or lossycompressed digital formats for content of various types, such as video,imaging, text, audio, etc. in the form physical states and/or signals,for example. Client computing device 302 may communicate with computingdevice 304 by way of a connection, such as an internet connection, vianetwork 308, for example. Although computing device 302 of FIG. 3 showsthe above-identified components, claimed subject matter is not limitedto computing devices having only these components as otherimplementations may include alternative arrangements that may compriseadditional components or fewer components, such as components thatfunction differently while achieving similar results. Rather, examplesare provided merely as illustrations. It is not intended that claimedsubject matter to limited in scope to illustrative examples.

Processor 320 may be representative of one or more circuits, such asdigital circuits, to execute at least a portion of a computing procedureand/or process. By way of example, but not limitation, processor 320 maycomprise one or more processors, such as controllers, microprocessors,microcontrollers, application specific integrated circuits, digitalsignal processors, programmable logic devices, field programmable gatearrays, the like, or any combination thereof. In implementations,processor 320 may execute signal processing to manipulate signals and/orstates, to construct signals and/or states, etc., for example.

Memory 322 may be representative of any storage mechanism. Memory 322may comprise, for example, primary memory 324 and secondary memory 326,additional memory circuits, mechanisms, or combinations thereof may beused. Memory 322 may comprise, for example, random access memory, readonly memory, etc., such as in the form of one or more storage devicesand/or systems, such as, for example, a disk drive, an optical discdrive, a tape drive, a solid-state memory drive, etc., just to name afew examples. Memory 322 may be utilized to store a program. Memory 322may also comprise a memory controller (not shown in FIG. 3) foraccessing computer readable-medium 340 that may carry and/or makeaccessible content, which may include code, and/or instructions, forexample, executable by processor 320 and/or some other unit, such as acontroller and/or processor, capable of executing instructions, forexample.

Under direction of processor 320, memory, such as memory cells storingphysical states, representing, for example, a program, may be executedby processor 320 and generated signals may be transmitted via theInternet, for example. Processor 320 may also receive digitally-encodedsignals from client computing device 302.

Network 308 may comprise one or more network communication links,processes, services, applications and/or resources to support exchangingcommunication signals between a client computing device, such as 302,and computing device 306 (‘third device’ in figure), which may, forexample, comprise one or more servers (not shown). By way of example,but not limitation, network 308 may comprise wireless and/or wiredcommunication links, telephone and/or telecommunications systems, Wi-Finetworks, Wi-MAX networks, the Internet, a local area network (LAN), awide area network (WAN), or any combinations thereof.

The term “computing device,” as used herein, refers to a system and/or adevice, such as a computing apparatus, that includes a capability toprocess (e.g., perform computations) and/or store content, such asmeasurements, text, images, video, audio, etc. in the form of signalsand/or states. Thus, a computing device, in this context, may comprisehardware, software, firmware, or any combination thereof (other thansoftware per se). Computing device 302, as depicted in FIG. 3, is merelyone example, and claimed subject matter is not limited in scope to thisparticular example. For one or more embodiments, a computing device maycomprise any of a wide range of digital electronic devices, including,but not limited to, personal desktop and/or notebook computers,high-definition televisions, digital versatile disc (DVD) players and/orrecorders, game consoles, satellite television receivers, cellulartelephones, wearable devices, personal digital assistants, mobile audioand/or video playback and/or recording devices, or any combination ofthe above. Further, unless specifically stated otherwise, a process asdescribed herein, with reference to flow diagrams and/or otherwise, mayalso be executed and/or affected, in whole or in part, by a computingplatform.

Memory 322 may store cookies relating to one or more users and may alsocomprise a computer-readable medium that may carry and/or makeaccessible content, including code and/or instructions, for example,executable by processor 320 and/or some other unit, such as a controllerand/or processor, capable of executing instructions, for example. A usermay make use of input device 318, such as a computer mouse, stylus,track ball, keyboard, and/or any other similar device capable ofreceiving user actions and/or motions as input signals. Likewise, a usermay make use of an output device, such as display 325, a printer, etc.,and/or any other device capable of providing signals and/or generatingstimuli for a user, such as visual stimuli, audio stimuli and/or othersimilar stimuli.

Regarding aspects related to a communications and/or computing network,a wireless network may couple client devices with a network. A wirelessnetwork may employ stand-alone ad-hoc networks, mesh networks, WirelessLAN (WLAN) networks, cellular networks, and/or the like. A wirelessnetwork may further include a system of terminals, gateways, routers,and/or the like coupled by wireless radio links, and/or the like, whichmay move freely, randomly and/or organize themselves arbitrarily, suchthat network topology may change, at times even rapidly. A wirelessnetwork may further employ a plurality of network access technologies,including Long Term Evolution (LTE), WLAN, Wireless Router (WR) mesh,2nd, 3rd, or 4th generation (2G, 3G, or 4G) cellular technology and/orthe like. Network access technologies may enable wide area coverage fordevices, such as client devices with varying degrees of mobility, forexample.

A network may enable radio frequency and/or other wireless typecommunications via a wireless network access technology and/or airinterface, such as Global System for Mobile communication (GSM),Universal Mobile Telecommunications System (UMTS), General Packet RadioServices (GPRS), Enhanced Data GSM Environment (EDGE), 3GPP Long TermEvolution (LTE), LTE Advanced, Wideband Code Division Multiple Access(WCDMA), Bluetooth, ultra wideband (UWB), 802.11b/g/n, and/or the like.A wireless network may include virtually any type of now known and/or tobe developed wireless communication mechanism by which signals may becommunicated between devices, between networks, within a network, and/orthe like.

Communications between a computing device and/or a network device and awireless network may be in accordance with known and/or to be developedcommunication network protocols including, for example, global systemfor mobile communications (GSM), enhanced data rate for GSM evolution(EDGE), 802.11b/g/n, and/or worldwide interoperability for microwaveaccess (WiMAX). A computing device and/or a networking device may alsohave a subscriber identity module (SIM) card, which, for example, maycomprise a detachable smart card that is able to store subscriptioncontent of a user, and/or is also able to store a contact list of theuser. A user may own the computing device and/or networking device ormay otherwise be a user, such as a primary user, for example. Acomputing device may be assigned an address by a wireless networkoperator, a wired network operator, and/or an Internet Service Provider(ISP). For example, an address may comprise a domestic or internationaltelephone number, an Internet Protocol (IP) address, and/or one or moreother identifiers. In other embodiments, a communication network may beembodied as a wired network, wireless network, or any combinationsthereof.

A device, such as a computing and/or networking device, may vary interms of capabilities and/or features. Claimed subject matter isintended to cover a wide range of potential variations. For example, adevice may include a numeric keypad and/or other display of limitedfunctionality, such as a monochrome liquid crystal display (LCD) fordisplaying text, for example. In contrast, however, as another example,a web-enabled device may include a physical and/or a virtual keyboard,mass storage, one or more accelerometers, one or more gyroscopes, globalpositioning system (GPS) and/or other location-identifying typecapability, and/or a display with a higher degree of functionality, suchas a touch-sensitive color 2D or 3D display, for example.

A computing and/or network device may include and/or may execute avariety of now known and/or to be developed operating systems,derivatives and/or versions thereof, including personal computeroperating systems, such as a Windows, iOS, Linux, a mobile operatingsystem, such as iOS, Android, Windows Mobile, and/or the like. Acomputing device and/or network device may include and/or may execute avariety of possible applications, such as a client software applicationenabling communication with other devices, such as communicating one ormore messages, such as via protocols suitable for transmission of email,short message service (SMS), and/or multimedia message service (MMS),including via a network, such as a social network including, but notlimited to, Facebook, LinkedIn, Twitter, Flickr, and/or Google+, toprovide only a few examples. A computing and/or network device may alsoinclude and/or execute a software application to communicate content,such as, for example, textual content, multimedia content, and/or thelike. A computing and/or network device may also include and/or executea software application to perform a variety of possible tasks, such asbrowsing, searching, playing various forms of content, including locallystored and/or streamed video, and/or games such as, but not limited to,fantasy sports leagues. The foregoing is provided merely to illustratethat claimed subject matter is intended to include a wide range ofpossible features and/or capabilities.

A network may also be extended to another device communicating as partof another network, such as via a virtual private network (VPN). Tosupport a VPN, broadcast domain signal transmissions may be forwarded tothe VPN device via another network. For example, a software tunnel maybe created between a logical broadcast domain, and a VPN device.Tunneled traffic may, or may not be encrypted, and a tunneling protocolmay be substantially compliant with and/or substantially compatible withany now known and/or to be developed versions of any of the followingprotocols: IPSec, Transport Layer Security, Datagram Transport LayerSecurity, Microsoft Point-to-Point Encryption, Microsoft's Secure SocketTunneling Protocol, Multipath Virtual Private Network, Secure Shell VPN,another existing protocol, and/or another protocol that may bedeveloped.

A network may communicate via signal packets and/or frames, such as in anetwork of participating digital communications. A broadcast domain maybe compliant and/or compatible with, but is not limited to, now knownand/or to be developed versions of any of the following network protocolstacks: ARCNET, AppleTalk, ATM, Bluetooth, DECnet, Ethernet, FDDI, FrameRelay, HIPPI, IEEE 1394, IEEE 802.11, IEEE-488, Internet Protocol Suite,IPX, Myrinet, OSI Protocol Suite, QsNet, RS-232, SPX, System NetworkArchitecture, Token Ring, USB, and/or X.25. A broadcast domain mayemploy, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX, Appletalk,other, and/or the like. Versions of the Internet Protocol (IP) mayinclude IPv4, IPv6, other, and/or the like.

Algorithmic descriptions and/or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processingand/or related arts to convey the substance of their work to othersskilled in the art. An algorithm is here, and generally, is consideredto be a self-consistent sequence of operations and/or similar signalprocessing leading to a desired result. In this context, operationsand/or processing involve physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical and/or magnetic signals and/or states capable of beingstored, transferred, combined, compared, processed or otherwisemanipulated as electronic signals and/or states representing variousforms of content, such as signal measurements, text, images, video,audio, etc. It has proven convenient at times, principally for reasonsof common usage, to refer to such physical signals and/or physicalstates as bits, values, elements, symbols, characters, terms, numbers,numerals, measurements, content and/or the like. It should beunderstood, however, that all of these and/or similar terms are to beassociated with appropriate physical quantities and are merelyconvenient labels. Unless specifically stated otherwise, as apparentfrom the preceding discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining”, “establishing”, “obtaining”,“identifying”, “selecting”, “generating”, and/or the like may refer toactions and/or processes of a specific apparatus, such as a specialpurpose computer and/or a similar special purpose computing and/ornetwork device. In the context of this specification, therefore, aspecial purpose computer and/or a similar special purpose computingand/or network device is capable of processing, manipulating and/ortransforming signals and/or states, typically represented as physicalelectronic and/or magnetic quantities within memories, registers, and/orother storage devices, transmission devices, and/or display devices ofthe special purpose computer and/or similar special purpose computingand/or network device. In the context of this particular patentapplication, as mentioned, the term “specific apparatus” may include ageneral purpose computing and/or network device, such as a generalpurpose computer, once it is programmed to perform particular functionspursuant to instructions from program software.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and/or storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change, such as atransformation in magnetic orientation and/or a physical change and/ortransformation in molecular structure, such as from crystalline toamorphous or vice-versa. In still other memory devices, a change inphysical state may involve quantum mechanical phenomena, such as,superposition, entanglement, and/or the like, which may involve quantumbits (qubits), for example. The foregoing is not intended to be anexhaustive list of all examples in which a change in state form a binaryone to a binary zero or vice-versa in a memory device may comprise atransformation, such as a physical transformation. Rather, the foregoingis intended as illustrative examples.

In the preceding description, various aspects of claimed subject matterhave been described. For purposes of explanation, specifics, such asamounts, systems and/or configurations, as examples, were set forth. Inother instances, well-known features were omitted and/or simplified soas not to obscure claimed subject matter. While certain features havebeen illustrated and/or described herein, many modifications,substitutions, changes and/or equivalents will now occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all modifications and/or changes as fallwithin claimed subject matter.

What is claimed is:
 1. A method of generating preference indices forimage content utilizing one or more special-purpose computing devices tooperate as a neural network and as another neural network, to operatewithout further human intervention, in which the one or morespecial-purpose computing devices includes one or more processors andone or more memory devices, comprising: accessing computer instructionsfrom the one or more memory devices of the one or more special-purposecomputing devices for execution on the one or more processors of the oneor more special-purpose computing devices; executing the accessedcomputer instructions utilizing the one or more computing devices; andstoring, in the one or more memory devices of the one or morespecial-purpose computing devices, any results of having executed theaccessed computer instructions on the one or more processors of the oneor more special-purpose computing devices, wherein the computerinstructions to be executed comprise instructions for generating aterm-independent preference index for a contiguous portion of an imageusing the another neural network, the another neural network includingneural network parameters developed for the neural network to identifyone or more object labels for a captured image, the another neuralnetwork including at least one classifier, not present in the neuralnetwork, to receive signal sample values from one or morefully-connected layers of the another neural network and to generatesignal sample values corresponding to the preference indices based, atleast in part, on the at least one classifier.
 2. The method of claim 1,wherein the term-independent preference index comprises a sentimentindex.
 3. The method of claim 1, wherein the term-independent preferenceindex comprises an odd number of allowed value levels.
 4. The method ofclaim 1, wherein the another neural network comprises a convolutionalneural network.
 5. The method of claim 4, wherein the convolutionalneural network comprises a deep convolutional neural network.
 6. Themethod of claim 1, wherein the neural network parameters developed forthe another neural network are transferred from the neural network. 7.The method of claim 1, wherein the generating the term-independentpreference index includes generating the term-independent preferenceindex via classification of signal samples generated by one or moreconvolutional layers of the another neural network.
 8. The method ofclaim 7, wherein the classification comprises classification via amachine learning process.
 9. The method of claim 8, wherein the machinelearning process comprises a support vector machine process.
 10. Themethod of claim 7, wherein the classification comprises classificationvia a regression process.
 11. An apparatus, comprising: one or morespecial-purpose computing devices to operate as a neural network and asanother neural network, the one or more special-purpose computingdevices including one or more processors and one or more memory devices,to execute computer instructions on the one or more processors, withoutfurther human intervention, the computer instructions to be executedhaving been accessed from the one or more memory devices for executionon the one or more processors, the one or more special-purpose computingdevices to store in the one or more memory devices any results to begenerated from the execution of the computer instructions on the one ormore processors; the computer instructions to be executed comprisinginstructions for execution of generating preference indices for imagecontent, wherein the computer instructions to be executed by the one ormore special-purpose computing devices further to comprise instructionsto: generate a term-independent preference index for a contiguousportion of an image using the another neural network, the another neuralnetwork to access neural network parameters developed for the neuralnetwork to identify one or more object labels for a captured image, theanother neural network including at least one classifier, not present inthe neural network, to receive signal sample values from one or morefully-connected layers of the another neural network and to generatesignal sample values corresponding to the preference indices based, atleast in part, on the at least one classifier.
 12. The apparatus ofclaim 11, the term-independent preference index is to comprise asentiment index.
 13. The apparatus of claim 11, wherein theterm-independent preference index is to comprise an odd number ofallowed value levels.
 14. The apparatus of claim 11, wherein the anotherneural network is to comprise a deep convolutional neural network. 15.The apparatus of claim 11, wherein the term-independent preference indexis to be generated via classification of signal samples generated by oneor more convolutional layers of the another neural network.
 16. Theapparatus of claim 11, wherein the term-independent preference index isto be generated via classification of signal samples generated by one ormore convolutional layers of the another neural network via a machinelearning process.
 17. An apparatus to generate a term-independentpreference index for image content utilizing one or more special-purposecomputing devices configured as a neural network and as another neuralnetwork, to operate without further human intervention, in which the oneor more special-purpose computing devices includes one or moreprocessors and one or more memory devices, comprising: means foraccessing computer instructions from the one or more memory devices ofthe one or more special-purpose computing devices for execution on theone or more processors of the one or more special-purpose computingdevices; means for executing the accessed computer instructionsutilizing the one or more computing devices; means for storing, in theone or more memory devices of the one or more special-purpose computingdevices, any results of having executed the accessed computerinstructions on the one or more processors of the one or morespecial-purpose computing devices; and means for utilizing developed forthe neural network, the neural network to identify one or more objectlabels for a captured image, to generate term-independent preferenceindices using the another neural network, the another neural networkincluding at least one classifier, not present in the neural network, toreceive signal sample values from one or more fully-connected layers ofthe another neural network and to generate signal sample valuescorresponding to the preference indices based, at least in part, on theat least one classifier.
 18. The apparatus of claim 17, furthercomprising means for classifying signal samples generated by one or moreconvolutional layers of the neural network.
 19. The apparatus of claim18, wherein the means for classifying the signal samples comprises meansfor classifying via a machine learning process.
 20. The apparatus ofclaim 17, further comprising means for generating an odd number ofallowed value levels.